Method And Apparatus For Interference Suppression In Orthogonal Frequency Division Multiplexed (OFDM) Wireless Communication Systems

ABSTRACT

A method and apparatus for interference suppression in wireless communication systems, especially Orthogonal Frequency Division Multiplexed (OFDM) systems, is presented. The array apparatus includes a two-tier adaptive array system, which provides for both spatial diversity and beamforming at the uplink and includes sub-arrays spaced at a distance sufficient to provide spatial diversity and support beamforming or scanning A Direction of Arrival (DOA) of signals impinging upon the array can be calculated by comparing signals from sub-array elements. Each sub-array can be filtered or beamformed to provide high gain to desired signals received from the DOA (which may be a multipath signal) while simultaneously dampening-out undesired signals, such as co-channel interference (CCI) in the frequency band of operation. The DOA is also used for allocating frequency bins for data signals, such as in an OFDM system, to provide weighted guidelines for bin allocation to maximize received signal power.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.13/269,336, filed Oct. 7, 2011, which is a is a continuation of U.S.application Ser. No. 09/975,518, filed Oct. 11, 2001, now U.S. Pat. No.8,050,288, issued Nov. 1, 2011, which is a continuation of Ser. No.09/106,884, filed Jun. 30, 1998, now U.S. Pat. No. 6,795,424, issuedSep. 21, 2004. The entire teachings of the above applications areincorporated herein by reference.

BACKGROUND OF THE INVENTION

In the last few years, the number of commercial cellular telephone usershas risen dramatically, but the bandwidth allocated to cellulartelephony has remained nearly constant. Because of the limited nature ofcellular telephony bandwidth as a resource, the cost of obtainingbandwidth has risen dramatically. This necessitates the efficientutilization of available bandwidth resources to maintain commercialviability.

Many intelligent schemes for optimizing the use of available bandwidthresources have been proposed. These methods include such means as signalcompression or elimination of non-essential frequency artifacts toreduce the overall bandwidth. Other systems include Time DivisionMultiple Access (TDMA) where multiple users utilize the same frequencyband by transmitting bursts of data in specified periodic time slots orCode Division Multiple Access (CDMA) systems.

The use of Orthogonal Frequency Division Multiplexing (OFDM) as amodulation and multiple access method for commercial wirelesscommunication systems is not widely practiced and is expected to grow inthe future. Potential applications include wireless local loop, wirelesslocal area networks and cellular and PCS systems. Possessing many of thebenefits of well known time and code division multiple access systems,OFDM based multiple access systems are also referred to as OrthogonalFrequency Division Multiple Access (OFDMA) systems in the literature.Recently, OFDM was chosen as the modulation scheme for the EuropeanDigital Audio Broadcast (DAB) standard and the European TerrestrialDigital Video Broadcast (DVB-T) standard. OFDM based hybrid multipleaccess systems such as OFDM-TDMA and Multicarrier-CDMA are also beingactively researched.

Such communication systems consist of a downlink and an uplink. Thedownlink is the unidirectional communication link from a singlebase-station (BS) to multiple remote (possibly mobile) transceivers. Theuplink is the unidirectional communication link from these transceiversto the BS. Typically, the downlink and uplink occupy distinctnon-overlapping frequency bands—also called frequency division duplex(FDD) operation. It is also possible to operate in time division duplex(TDD) (“ping-pong” or half-duplex mode) where the uplink and downlinkoccupy the same frequency band but alternate in time. This is generallypreferred only for indoor systems. The uplink is a multiple accesschannel since the plurality of remote transceivers access or share theuplink channel resources. The downlink can be thought of as a broadcastor multicast link. In general, the problem of interference suppressionis more difficult and important for the uplink since typically itrepresents the capacity bottleneck (compared to the downlink).

One of the major problems faced by wireless communication systems isthat of interference. In particular, in OFDM systems, two maincategories of interference are Inter-Bin Interference (IBI) andCo-channel Interference (CCI). IBI is the manifestation of loss oforthogonality between different bins of a OFDM system. Each datacarrying bin acts as a source of interference (or noise) for every otherdata carrying bin. CCI refers to any other undesired signal whosespectrum overlaps with the spectrum of the particular OFDM system underconsideration and causes interference. For example, sources of CCI maybe other analog or digital communication/broadcast systems (which may ormay not be using OFDM) operating in the same (or adjacent) frequencyband in the same/nearby geographic areas. IBI and CCI can increase thebit-error-rate of the particular frequency bins that are experiencingthe interference. As a result, the OFDM system performance may bedegraded. Thus, interference suppression techniques are desirable forhigh-performance systems. A number of different techniques have beenprepared to either avoid or suppress interference.

A factor which must be considered in multiple access wireless systems isthat of power control or automatic gain control (AGC). Essentially, thereceiver must be able to ensure that the received power of each bin iswithin a certain target range. This problem is made difficult by thepresence of fading which can easily cause fluctuations in the receivedpower in the range of 20-40 dB in a matter of seconds. Thus, in wirelesssystems, some basic power control mechanisms may be used. However, thesepower control mechanisms may not be perfect. Imperfect power control mayexacerbate the effect of IBI.

If CCI is localized in frequency (i.e., narrowband CCI), the particularbin (or bins) that are affected such that the average signal-tointerference-plus-noise ratio (SINR) is reduced below a certainthreshold can be left unused. If the interference is temporary, the bincan be reused when the SNR improves. The basic procedure is wellestablished in digital subscriber line (DSL) modems which use DMT as themodulation scheme. This procedure may be implemented by the BS in awireless OFDM system by measuring any CCI across the frequency band ofinterest. However, the problem is more difficult in wireless systemsbecause of the presence of fading which can also greatly reduce the SNR.Thus, the average SNR must be tracked. Fading results in fluctuations inthe channel frequency response with time.

One measure of the rate of change of the channel response with time isgiven by the so-called Doppler spread (units of Hertz). When there islittle relative movement between the receiver and transmitter (or whenthe propagation environment is relatively static), the multipath fadingcan be considered to be slow fading and the Doppler spread is around 5Hz or less (this is not to be confused with the attenuation due todistance which also changes slowly, typically according to thelog-normal distribution). In such cases (e.g. wireless local loop andindoor systems), the receiver can track and estimate the channelfrequency response for each bin with good accuracy. This is typicallyaccomplished via the use of periodic pilot sub-symbols inserted in thesub-symbol streams of each bin of interest. F or example, for a givendata carrying bin, every pth (say p=8 or 16) sub-symbol can be pilot(training) sub-symbol to estimate the channel periodically. Forin-between sub-symbols, the receiver can estimate the channel byoperating in decision directed mode or by interpolation. For fast fadingchannels (Doppler spread 10-200 Hz), estimating the channel is moredifficult and sophisticated time-frequency interpolation techniques mustbe used (this is a drawback of OFDM).

Several techniques have been proposed in the literature for combatingIBI and/or CCI. One method of addressing IBI is to space data carryingbins apart in frequency and leave bins unused there between. This iseffective because: (a) the effect of IBI decreases with increase infrequency separation between bins and, (b) for a given total bandwidth,there are fewer active bins. However, this is wasteful of bandwidth andnot a preferable solution. Another approach for addressing IBI and CCIis forward error correction (FEC) codes, mostly implemented inconjunction with interleaving. FEC codes may afford some protectionagainst noise and interference. A related method is the use of Trelliscoded modulation (TCM) to address IBI and CCI. However, the methodsproposed heretofore have met with limited success. A need remains for animproved method and apparatus to overcome the problems associated withIBI and CCI.

One aspect of the present invention is targeted at interferencesuppression in the uplink of a FDD OFDMA system using spatial signalprocessing via antenna arrays deployed at the BS receiver. The presentinvention is not limited to FDD OFDMA, but may be to carry outinterference suppression in other scenarios as well such as for hybridOFDM-TDMA systems: Multicarrier-CDMA systems, TDD systems and in thedownlink of the above systems.

SUMMARY OF THE INVENTION

The present invention affords a method and apparatus for suppression ofCCI via the use of receive antenna arrays at the BS for the uplinkchannel. In addition, those skilled in the art will recognize that theapplication of this invention is not limited to the BS uplink channel,but is also applicable elsewhere including the BS downlink and at theremote transmitter/receivers. The intelligent use of antenna arrays formitigating fading and interference is also referred to as “smart” or“intelligent antennas”. Smart antenna systems may be carried out throughthe use of switched beam antennas or adaptive arrays (AA). Switched beamantennas use a fixed beamforming network to provide several output portscorresponding to beams in fixed directions. Signal levels in each beamare monitored and analyzed to switch the beams appropriately amongdifferent time or frequency channels depending on the air-interfacescheme. Adaptive arrays, on the other hand, electronically steer aphased array by weighting the amplitude and phase of signal at eachelement in response to changes in the propagation environment. Adaptivearrays provide greater steering flexibility in response to thepropagation environment. The preferred embodiment focuses on adaptivearrays. However, switched beam antennas may be used.

A first inventive aspect of the present invention involves an adaptivearray (AA) architecture and methods for combating the effect of IBI andCCI over multipath fading channels. As described above, the solution ispresented for the uplink of an OFDMA system with synchronous uplink,however, the present invention is not limited to OFDMA. A secondinventive aspect involves a method for allocation of frequency bins(i.e., determining the spectral locations or bin numbers) to differentusers by taking spatial and other information (such as automatic gaincontrol (AGC) information) into account. Each user may require one ormore bins to meet a certain quality of service requirement. This aspectof the invention is most appropriate in the context of the adaptivearray architecture described above but, in general, is not limited tosuch a receiver configuration as will be apparent to those skilled inthe art.

For multi-element AA receivers, each element may have its ownRF-to-baseband conversion and baseband demodulator. Of course, hardwareoptimizations may be possible. All beamforming and diversity combiningalgorithms operate on digital complex baseband signals, for instance viageneral purpose or application specific DSP's, ASIC's, in software (suchas in software radios) or combinations thereof.

Consider slowly time-varying fading channels (SFC) first. This impliesthat the channel attenuation coefficients in each frequency bin can betaken to be constant over each symbol (a single complex number). Also,these coefficients may change from symbol to symbol but at a slow raterelative to the symbol rate. Some systems predominantly encounter SFC(e.g. indoor systems) while others encounter FFC's (e.g. cellularsystems along highways). However, most wireless communication systems ofinterest experience a mix of SFC and FFC conditions.

Generally, wireless communications systems experience two primary typesof signal fading within channels, slow time varying fading and fast timevarying fading. The IBI due to the time-varying fading nature of achannel is negligible. IBI due to frequency offsets (imperfectsynchronization) is still possible. A preferred embodiment of thepresent invention includes a AA with the elements spaced far apart (5 to15 wavelengths) to obtain spatial diversity, i.e., independent fading atdifferent antenna elements. The combining method of the preferredembodiment uses maximal ratio combining (MRC) to correct for IBI andAdditive white Gaussian noise (AWGN). The MRC is merely a spatialmatched filter. If an M element array is used, each bin has a separate Mdimensional combining weight vector. To implement MRC, the channelfrequency response for each bin may be estimated via periodic pilotsub-symbols. Note that the MRC also subsumes the role of the standardfrequency equalization (FEQ) operation.

If CCI is also present, an optimal method (i.e., according to onespecific criterion) is the so-called maximum SINR optimum combining(MSOC). This method also uses channel estimation. In addition, somestatistics of the signal and interference must also be estimated.Periodic pilot sub-symbols can be used for both these tasks. MSOC hasgreater computational burden than MRC but greater potential forperformance improvement.

For fast time-varying channels, implementing MSOC may require excessivebandwidth overhead for pilot sub-symbols. Basically, if MSOC isimplemented on a FFC, it may not give any more benefit than if MRC wasused. In fact, the performance can worsen if the channel coefficientsare not tracked properly. Unfortunately, the method required to remedythis requires a different receiver architecture than above. Therefore,the receiver architecture has to be chosen to be one of the two and itmay not be possible to change it on the fly unless the BS has a flexiblesoftware radio architecture. To avoid the foregoing problems, thepreferred embodiment proposes the two-stage method as described below.

First, the antenna array is partitioned into sub-arrays. The elements ofeach sub-array are spaced close together (e.g., half wavelength or lessto avoid spatial aliasing or grating lobes) to facilitate beamforming.Second, the individual sub-arrays are spaced far apart (e.g., 5-15wavelengths) to obtain spatial diversity. The preferred embodiment useseach sub-array for beamforming for CCI suppression. Since thepost-beamforming outputs from each sub-array may be largely affected bynoise only, they can be diversity combined. Diversity combining can bedone using MRC (which requires channel estimation). But channelestimation is expected to be easier in the second stage since the firststage is expected to greatly reduce the interference. No FEQ is requiredsince the MRC essentially serves the role of a “multi-element” FEQ. IfMRC is not feasible, switched diversity combining (SDC) may be used bymeasuring the instantaneous SNR for each sub-array's output andselecting the “best” option each symbol time (other variations are alsopossible). In this case, a standard FEQ is still required. As mentionedearlier, techniques for channel estimation via time-frequency patternsof pilots can be used in conjunction with the proposed AA architectureand an overall improvement in performance and/or reduction incomplexity/bandwidth overhead of those algorithms is expected. Note thatthe use of this two-stage architecture and method is not limited tooperation over FFC only and can be used over SFC as well instead of MSOC(but MSOC is very difficult to implement over FFC). Thus, the two-stagemethod can be considered to be more general. Nor is the number ofelements in each sub-array or the number of sub-arrays limited.Increasing the number of elements in each sub-array can provide formore-optimal beamforming while increasing the number of sub-arraysoutputs that are diversity combined can serve to further reduceinterference. As an example, a preferred embodiment may have eachsub-array made up of 4-8 elements with 2 sub-arrays for a total of 8-16elements.

In addition, beamforming in stage 1 can be done according to any one ofa number of criterion. The preferred embodiment usesdirection-of-arrival (DOA) based constraints for beamforming. DOA basedconstraints may be used when signals are directional such as in rural orsuburban environments, but are less desirable when the angle spread islarge (such as in indoor environments).

A number of methods may be used for DOA estimation. For example, someremote transmitters may be equipped with GPS type equipment to enablethe BS to compute this information. Other possible methods are the useof BS triangulation via time-difference-of-arrival (TDOA) measurements.One method to estimate DOA's of a given user is by using adjacentantenna array elements in each sub-array. The idea here is to extractthe phase differences between complex baseband (symbol rate) samplesfrom adjacent sensors or doublets. As mentioned above, the sensors ineach sub-array are spaced a half-wavelength apart or closer to avoidspatial aliasing. For a range of channel scenarios, the fadingexperienced by adjacent sensors is almost perfectly correlated. For therange of signal bandwidths and RF carrier frequencies, the signals canbe considered to be narrowband. Thus, when the signals are coherentlydownconverted and demodulated, a mutual phase offset is induced betweenthe samples obtained from adjacent sensors. This phase offset isproportional to the inter-sensor spacing (normalized in wavelengths) andthe sine of the DOA measured with respect to the normal to the array.This spatially induced phase offset is not only obtained for successivesymbols, but also for all bins being used by a particular user. Thus,the measured phase offset can be smoothed in space (over multipledoublets in each sub-array and using multiple sub-arrays), in time (overa block of symbols), and in frequency (over multiple bins used by thesame user) to mitigate the effect of noise. Note that since all arrayprocessing algorithms operate on complex baseband outputs, theprocessing can be efficiently done in the frequency domain. Also notethat the AA receiver structures may also be implemented in conjunctionwith sectorized cells. For example, each cell can have 3-6 sectors andan AA receiver can be used within each sector. Of course, one of theadvantages of AA in cellular/PCS systems is to achieve a reduction inthe number of sectors per cell (which will improve the trunkingefficiency) and still derive benefits of spatial separation betweensignals.

Generally, according to the preferred embodiment, the BS attempts toallocate bins to facilitate or augment the mitigation of IBI and CCI. Inthis method, the BS continually monitors a number of parameters and usesthem to compute the bin allocations for a given user. Such allocationsare typically made at start-up, but may also be made on-the-fly fornon-constant bit-rate type applications. The allocations can also bechanged dynamically in response to changes in the prevalent noise andinterference conditions. For operation over SFC, deep fades may occurover portions of the signal spectrum (perhaps spanning several bins) forextended periods of time, perhaps seconds or even minutes. Bins can alsobe dynamically reassigned in such cases.

To be more specific, consider a system where signals are spatiallylocalized (e.g. most cellular/PCS rural/suburban systems). Assume thatthe BS has estimates of the direction and received power of the dominantsignal paths of all active users. Due to multipath communications, eachuser may have more than one distinct (and strong) multipath directions.The BS will typically set a limit on the number of dominant paths thatit can take into account (such as 2 or 3) due to constraints incomputation/memory etc. Such estimates can be computed as per discussionabove or by pilots embedded in the sub-symbol streams of users.Similarly, the BS also computes the power and directions of CCI acrossthe band of interest. For example, if the two stage receiverarchitecture described above is used, these directions and powers can beobtained from the beamforming coefficients used in each sub-array.

For example, assume that a particular user is to be allocated K bins.Using these inputs, the BS allocates bins to satisfy the following(desired) criteria:

1. The K bins belonging to any one user should be spaced as far apart infrequency as possible to minimize mutual IBI. Spacing the bins belongingto each user over a wide range of frequencies within the band alsoprovides frequency diversity. Frequency diversity is desirable becauseit serves to lessen the effects of fading over a certain frequencyrange. For example, by allocating many widely spaced frequency bins to asingle user, if the operating environment is such that some of the binsexperience fading, the overall signal quality will still remain highbecause the other user bins will not experience this fading; in short,fading over a small frequency range within the band will not effect thewhole signal.

2. Each bin is placed in a neighborhood with bins belonging to otherusers which are spaced as far apart as possible in the dominant DOAs oftheir signals. For example, a 3-5 bin neighborhood is expected to besuitable for most applications.

3. Each bin is placed in a neighborhood with bins belonging to otherusers such that differences in signal strength of active bins in theneighborhood are minimized.

4. Each bin is placed in a spectral location such that there are noco-channel interferers in the same frequency band. If no such locationsare available, spectral locations are chosen based on the DOA of the CCIand the signal strength of the CCI. In general, CCI bins with lower CCIsignal strength are assigned before bins with higher CCI signalstrength. Also, bins are allocated so that the difference in the DOA'sof the particular user and the CCI are as large as possible. Thesecriteria are balanced depending upon the operating environment.

These criterion lead to better separation of potentially interferingsignals in the spatial domain, thus facilitating the operation ofspatial interference suppression techniques. The above criteria may be“weighted” differently to construct algorithms or flowcharts optimizedfor a specific (or category of) channel and interference scenarios, aswill be apparent to those skilled in the art. For example. If IBI is thedominant impairment, items 1 and 2 above can be given the mostimportance. Item 3 is important if the system is operated in anenvironment with a wide range of received power levels. Similarly, ifCCI is the dominant impairment, item 4 is given the highest priority.Since the number of bins in an OFDM can be quite large (several hundredbins is common), the implementation of the overall algorithm must bereasonably simple to enable execution in real-time. For instance, onepossible way to implement it would be to construct a data structurewhich contains a table of information about each bin. This would includeinformation such as whether the bin is active or inactive (at any giventime), the user occupying the bin if any, whether it is a data, controlor pilot bin, modulation scheme and constellation size of sub-symbols inthe bin, received power level, dominant DOA's of the user occupying thebin, power level and DOA's of any co-channel interferers spectrallyoverlapping with the bin, etc. Note that all items of the aboveinformation may not be available or be able to be computed for each binat all times. Certain of the above factors (like received power levels)appear to be best suited for update on a periodic (scheduled) basis(such as every n milliseconds or during each frame as per some existingframing structure). Other items are better suited for update in an eventdriven mode (such as user activity and constellation size), i.e., when auser arrives, departs, requests (or is forced to have) a change in theamount of allocated bandwidth.

Turning now to a more rigorous examination of uplink multiple accessusing OFDM. In an OFDMA system, the entire uplink bandwidth processed bya base-station is (dynamically) allocated among a group of users. Whilethe downlink is always synchronous, unlike uplink TDMA systems in whichremote units transmit in bursts in specified periodic time-slots, uplinkOFDMA can be made synchronous using the method of loop-timing. In thismethod, each mobile transceiver first synchronizes itself to thebase-station on the downlink and then derives its uplink transmittertiming reference from the recovered downlink clock. To facilitate theformer task, the base-station embeds pilot tones in the transmitteddownlink signal which are utilized by the remote receiver to “lock-on”to the base's timing reference. To overcome frequency selective fadingacross the signal bandwidth, multiple pilots can be used. Whileconventional baseband digital phase locked loops can be used foroperation over slowly time-varying channels, for frequency acquisitionand tracking algorithms suitable for operation over fast time-varyingchannels. The local timing reference for mobile transceivers are usuallyderived from a Voltage Controlled Crystal Oscillator (VCXO) whichprovides the timing reference for the receiver A/D, transmitter D/A andall radio frequency (RF) circuitry. Frequency offsets between thereceive and transmit symbol clock occur due to non-idealities in theremote transceiver VCXOs, possibly of the order of severalparts-per-million (ppm).

Assuming that the initial tasks of carrier frequency synchronization,symbol timing recovery and symbol time alignment have been completedthis enables the base-station receiver to demodulate received basebandsignals from all users with a single FFT. The base-station is alsoresponsible for all bandwidth management functions to provide each unitwith shared access to the uplink channel.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIGS. 1a-1c illustrate a preferred embodiment of the adaptive antennaarray architecture implemented for use in a cellular telephone systemaccording to the present invention.

FIGS. 2a-2b illustrate a preferred embodiment of the sub-array of theadaptive antenna array architecture of FIG. 1.

FIG. 3 illustrates, graphically, a method for determining the directionof arrival of received signals or co-channel interference.

FIGS. 4a and 4b illustrate exemplary signal response patterns of theadaptive antenna array as modified according to the present invention tofocus the gain at multi-path reception points and reduce the gain atpoints of co-channel interference.

FIG. 5 is a general block diagram of the adaptive antenna arrayarchitecture of the embodiment of FIG. 1.

FIG. 6 illustrates a typical environment for operation of the adaptiveantenna array architecture of the embodiment of FIG. 1 including thegeographic positioning of multiple users and co-channel interference.

FIG. 7 illustrates the manner by which frequency bins are allocated bythe adaptive antenna array architecture in the frequency domainaccording to one embodiment of the present invention.

FIG. 8 is a block diagram of one exemplary method for frequency binallocation of the preferred embodiment of the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENT

Initially, an explanation is provided of an OFDM transmission modelbefore describing the preferred embodiments. Consider a discrete timeOFDM system model in which N orthogonal sinusoids are transmitted eachsymbol time. The N bins are partitioned among a total of L independentusers in non-overlapping sets of K

N/L (assumed integer) bins for each user. Without loss of generality, acomplex baseband representation is used for all signals. Thus, thenormalized transmitted signal from the lth user is given by

$\begin{matrix}{{{S_{l}(n)} = {\frac{1}{\sqrt{N}}{\sum\limits_{k\; \varepsilon \; S_{l}}\; {{a(k)}^{j\frac{2\pi}{N}{kn}}}}}},{n\; {\varepsilon \lbrack {0,{N - 1}} \rbrack}},{l\; {\varepsilon \lbrack {0,{L - 1}} \rbrack}}} & (2.1)\end{matrix}$

where j=√{square root over (−1)}; a(k) is the kth frequency domainsub-symbol typically selected from a quadrature amplitude modulation(QAM) constellation, and S_(l) denotes the set of bin indices belongingto the lth user. With a sample rate f_(s), assume that each userencounters a time-selective multipath fading channel impulse response(CIR) spanning a maximum duration T_(CIR)=U/f_(s). Thus, the receivedsignal after passing through the channel is given by

$\begin{matrix}{{{x_{1}(n)} = {\sum\limits_{u = n}^{n - U + 1}\; {{h_{u,l}(n)}{s_{l}(u)}}}},{l\; {\varepsilon \lbrack {0,{L - 1}} \rbrack}}} & (2.2)\end{matrix}$

where h_(u,l)(n) denotes the CIR for the lth user at time n due to theimpulse δ(n−u). Note that slowly time-varying channels may be consideredto be time-invariant over a symbol period leading toh_(u,l)(n)=h_(l)(n−u). Using (2.1) in (2.2),

$\begin{matrix}{{{x_{1}(n)} = {\frac{1}{\sqrt{N}}{\sum\limits_{u = 0}^{U - 1}\; {{h_{{n - u},l}(n)}{\sum\limits_{k\; \varepsilon \; S_{l}}\; {{a(k)}^{j\frac{2\pi}{N}{k{({n - u})}}}}}}}}},{n\; {\varepsilon \lbrack {0,{N - 1}} \rbrack}},{l\; {\varepsilon \lbrack {0,{L - 1}} \rbrack}}} & (2.3)\end{matrix}$

Each symbol is prefixed with a cyclic prefix (CP) or guard time. The CPserves two main purposes. First, inter-symbol-interference is eliminatedwhen the CP length is chosen to be longer than the CIR. Second, byappropriately selecting the transmitted samples of the CP, thetransmitted signal appears periodic to the channel resulting insimplified frequency-domain equalization. This is done by setting

s _(l)(−j)=s _(l)(N−j), jε[1,G],lε[0,L−1]  (2.4)

where G is chosen such that T_(CP)≧T_(CIR), i.e. G≧U. In addition, theCP can also be utilized for synchronization purposes. At the receiver,the CP samples are discarded before demodulation and it is sufficient toconsider each symbol independently. The received uplink signal at thebase-station from all users is given by

$\begin{matrix}{{r(n)} = {{\sum\limits_{l = 0}^{L - 1}\; {x_{l}(n)}} + {v(n)} + {z(n)}}} & (2.5)\end{matrix}$

where v(n) denotes discrete-time AWGN samples with variance σ_(v) ² andz(n) denotes discrete-time CCI samples. After discarding the CP, (2.5)can be written compactly in matrix form for an entire symbol as,

$\begin{matrix}{r = {{\sum\limits_{l = 0}^{L - 1}\; {\sum\limits_{u = 0}^{U - 1}\; {H_{u,l}T_{l}D_{u,l}a_{l}}}} + v + z}} & (2.6)\end{matrix}$

where r=[r(0), r(1), . . . , r(N−1)]^(T) is the received signal vector;a_(l)εC^(K); denotes the column vector of frequency domain sub-symbolsfrom the lth user; v=[v(0), v(1), . . . , v (N−1)]^(T)εC^(N); z=[z(0),z(1), . . . , z(N−1)]^(T)εC^(N); H_(u,l)=diag([h_(−u,l)(0),h_(−u+l,l)(1), . . . , h_(−u+N−1,l)(N−1)])εC^(N×N), denotes the diagonalCIR matrix for the lth user and uth delay; D_(u,l)εC^(K×K) denotes adiagonal matrix of phase delays whose element corresponding to the kthbin is given by

$^{{- j}\frac{2\pi}{N}{ku}}$

and T_(l)εC^(N×K) denotes the inverse-DFT modulating matrix whose columncorresponding to the kth bin is given by

$t_{l,k} = {{\frac{1}{\sqrt{N}}\lbrack {1,^{j\frac{2\pi}{N}k},{\ldots \mspace{14mu} ^{j\frac{2\pi}{N}{k{({N - 1})}}}}} \rbrack}^{T}.}$

Turning our attention to the slowly time-varying channel, we begin byconsidering a slowly varying fading channel in which the impulseresponse may be taken to be time-invariant over a symbol period. Thiscase illustrates the demodulating procedure and will also serve as astarting point for the rest of the analysis. Thus, H_(u,l)=h_(l)(u)I_(N)where h_(l)(u) is the uth impulse response coefficient. Using (2.6), thepth bin belonging to, say the lth user, is demodulated as

y(p)=t _(l,p) ^(H) r=a _(s)(p)a(p)+{circumflex over (v)}(p)+{circumflexover (z)}(p), pεS _(l)  (2.7)

where,

$\begin{matrix}{{a_{s}(p)} = {\sum\limits_{u = 0}^{U - 1}\; {{h_{l}(u)}^{{- j}\frac{2\pi}{N}{up}}}}} & (2.8)\end{matrix}$

and {circumflex over (v)}(p) and {circumflex over (z)}(p) denote thepost-demodulation residual noise and CCI respectively. Thus, there is nofading induced IBI in this case. There are several methods forfrequency-domain equalization ranging from no equalization at all (forinstance, using differential-phase-shift-keying schemes such as D-QPSK),to sophisticated time-frequency adaptive filtering algorithms. Theformer are usually sufficient for slowly time-varying channels while thelatter are used in more demanding environments.

Now consider the fast time-varying channel. As above, using (2.6), thepth bin belonging to, say the lth user, is demodulated as

$\begin{matrix}{{y(p)} = {{\sum\limits_{l = 0}^{L - 1}\; {\sum\limits_{u = 0}^{U - 1}\; {t_{l,p}^{H}H_{u,l}T_{l}D_{u,l}a_{l}}}} + {t_{l,p}^{H}( {v + z} )}}} \\{= {{{{a_{f}(p)}{a(p)}} + {i_{f}(p)} + {\hat{v}(p)} + {{\hat{z}(p)}\mspace{14mu} p}} \in S_{l}}}\end{matrix}$

where,

${a_{f}(p)} = {\frac{1}{N}{\sum\limits_{u = 0}^{U - 1}\; {\sum\limits_{n = 0}^{N - 1}\; {{h_{{n - u},l}(n)}^{{- j}\frac{2\pi}{N}{up}}}}}}$${i_{f}(p)} = {\frac{1}{N}{\sum\limits_{l = 0}^{L - 1}\; {\sum\limits_{{k \in S_{l}}{k \neq p}}^{\;}\; {\sum\limits_{u = 0}^{U - 1}\; {\sum\limits_{n = 0}^{N - 1}\; {{h_{{n - u},l}(n)}^{{- j}\frac{2\pi}{N}{n{({k - p})}}}^{{- j}\frac{2\pi}{N}{uk}}{a(k)}}}}}}}$

It is straightforward to show that the variance of {circumflex over(v)}(p) equals σ_(v) ² and the post-demodulation CCI variance is givenby,

${\sigma_{z}^{2}(p)} = {{E\lbrack {{\hat{z}(p)}}^{2} \rbrack} = {{\sum\limits_{n = {- {({N - 1})}}}^{N - 1}\; {( {1 - {{n}/N}} ){r_{z}(n)}^{{- j}\frac{2\pi}{N}{up}}}} = {{{P_{z}(w)}*\frac{\sin^{2}( {{Nw}/2} )}{{N\; {\sin^{2}( {w/2} )}}\;}}_{w = \frac{2\pi \; p}{N}}}}}$

where E[•] denotes the ensemble expectation; r_(z)(•) is the CCIdiscrete time auto-correlation function and P_(z)(w) is the CCI powerspectral density (PSD). Thus, for a given, say pth bin, thepost-demodulation CCI is given by the convolution of the CCI PSD with asinc²(•) function evaluated at the corresponding angular frequency. Eachdemodulated sub-symbol is now corrupted by IBI from all othersub-symbols. The effect of IBI is damaging for even small values ofDoppler spreads and frequency offsets and can severely limit the biterror rate performance. If the tone interferer is f_(t) Hz away from thecenter of a particular bin, the normalized frequency offset isf_(t)/f_(bin). This shape of the spectral leakage function is a directconsequence of using the discrete Fourier transform as the OFDMmodulation basis function.

Consider now the effect of time-varying random frequency offsets due toimperfect transceiver synchronization and phase noise. This issuebecomes particularly important when the synchronization problem isaggravated by fast time-varying channels, or for systems that aresensitive to power and complexity considerations. Let the normalizedfrequency offset be denoted by η=f_(off)/f_(bin) where the inter-binspacing f_(bin)=f_(s)/N. Each symbol time, η is modeled as a realizationof an independent uniformly distributed random variable in the interval[−η_(MAX),η_(MAX)]. Thus, the demodulating vector with frequency offsetis given by J(η) t_(l,p) where J(η)εC^(N×N) denotes a diagonal offsetmatrix with nth element given by

$^{j{({{\frac{2\pi}{N}n\; \eta} + \beta})}}$

where βε[−π,π] denotes a phase offset varying from symbol to symbol.Thus,

$\begin{matrix}{{{{y(p)} = {{{\sum\limits_{l = 0}^{L - 1}{\sum\limits_{u = 0}^{U - 1}{t_{l,p}^{H}{J^{H}(\eta)}H_{u,l}T_{l}D_{u,l}a_{l}}}} + {t_{l,p}^{H}{J^{H}(\eta)}( {v + z} )}} = {{{a_{f,\eta}(p)}{a(p)}} + {i_{f,\eta}(p)} + {{\hat{v}}_{\eta}(p)} + {{\hat{z}}_{n}(p)}}}},{p \in S_{l}}}\mspace{20mu} {{where},\mspace{20mu} {{a_{f,\eta}(p)} = {\frac{1}{N}{\sum\limits_{u = 0}^{U - 1}{\sum\limits_{n = 0}^{N - 1}{{h_{{n - u},l}(n)}^{j{({{\frac{2\pi}{N}n\; \eta} + \beta})}}^{{- j}\frac{2\pi}{N}{up}}}}}}}}} & (2.9) \\{{i_{f,\eta}(p)} = {\frac{1}{N}{\sum\limits_{l = 0}^{L - 1}{\sum\limits_{{k \in S_{l}}{k \neq p}}^{\;}\; {\sum\limits_{u = 0}^{U - 1}\; {\sum\limits_{n = 0}^{N - 1}{{h_{{n - u},l}(n)}^{j{({{\frac{2\pi}{N}{n{({k - p + \eta})}}} + \beta})}}^{{- j}\frac{2\pi}{N}{uk}}{a(k)}}}}}}}} & (2.10)\end{matrix}$

The SINR for the pth bin is defined as

$\begin{matrix}{{{SINR}(p)} = \frac{E\lbrack {{{a_{f,n}(p)}{a(p)}}}^{2} \rbrack}{{E\lbrack {{{i_{f,n}(p)} + {{\hat{z}}_{\eta}(p)}}}^{2} \rbrack} + \sigma_{v}^{2}}} & (2.11)\end{matrix}$

Assuming a wide sense stationary uncorrelated scattering (WSSUS)multipath fading model and a Rayleigh fading Doppler spectrum [13],expressions for signal and interference powers in (2.11) appear in theAppendix.

We now turn to an analysis of the reception of the antenna arrayreceiver. In conventional single-antenna wireless OFDM receivers, FFTbased demodulation is generally followed by frequency domainequalization (FEQ) and subsequent mapping of the equalized frequencydomain sub-symbols to bits. In an adaptive array OFDM receiver,demodulator outputs from each sub-array element are fed into a bank ofarray combiners where a separate array combining vector is used for eachbin. We propose the use of the maximum SINR criterion and constraintbased beamforming for weight adaptation under appropriate channelconditions as described later in this section. In the former method,channel estimation is necessary and antennas are widely spaced to obtainindependent fading between elements (i.e., spatial diversity). Tomaximize the SINR at the output of the array, the optimum weight vectorbalances diversity and interference suppression. In the latter approach,antenna elements are spaced sufficiently close to prevent spatialaliasing (i.e., grating lobes) and facilitate the application ofconstraints, such as those derived from estimates ofdirection-of-arrival (DOA) of impinging signals (i.e., the four factorsdiscussed above).

Considering reception over slowly time-varying. For a given data rateand bandwidth, the ratio of symbol rate to fading rate (or Dopplerspread) in OFDM is much smaller compared to single-carrier systems. Forexample, with a typical OFDM symbol rate of 4 KHz and with a Dopplerspread of 200 Hz, the ratio is 20 (the same ratio for the IS-136 TDMAsystem having a 24.3 KHz symbol rate is about six times greater). Forthe maximum SINR method to be effective, the temporal averaging used toestimate the noise-plus-interference statistics must be done over a timeduration much smaller than the duration over which the fading changessignificantly. Channel estimates for each antenna element are alsorequired. Moreover, this procedure has to be carried out separately forevery data carrying bin. Thus, this approach is suitable only for OFDMsystems with slow time-varying fading, for example in low mobilityscenarios.

Thus, we utilize statistically optimum array combining as per themaximum SINR criterion for operation over slowly fading channels. Withan M element array receiver depicted in FIG. 5, let the CIR matrix forthe mth element user be denoted by H_(u,l) ^((m))=h_(l) ^((m))(u)I_(N).The array elements are spaced sufficiently apart to obtain uncorrelatedfading. The corresponding received signal is given by

$\begin{matrix}{r^{(m)} = {{\sum\limits_{l = 0}^{L - 1}{\sum\limits_{u = 0}^{U - 1}{{h_{l}^{(m)}(u)}T_{l}D_{u,l}a_{l}}}} + v^{(m)} + {\sum\limits_{e}\; z_{e}^{(m)}}}} & (3.1)\end{matrix}$

where z_(e) ^((m)) denotes the eth CCI component received at the mthsub-array. Thus, the demodulated signal at the output of the mth elementis given by

${y^{(m)}(p)} = {{{\sum\limits_{l = 0}^{L - 1}{\sum\limits_{u = 0}^{U - 1}{t_{l,p}^{H}{J^{H}(\eta)}{h_{l}^{(m)}(u)}T_{l}D_{u,l}a}}} + {t_{l,p}^{H}{J^{H}(\eta)}( {v^{(m)} + {\sum\limits_{e}\; z_{e}^{(m)}}} )}} = {{{a_{s,\eta}^{(m)}(p)}{a(p)}} + {i_{s,\eta}^{(m)}(p)} + {\hat{v}}_{\eta}^{(m)} + {\sum\limits_{e}\; {{\hat{z}}_{e,\eta}^{(m)}(p)}}}}$

where a_(s,η)(p) and i_(s,n) ^((m))(p) are given by setting h_(n−u,1)(n)to h_(l) ^((m))(u) in (2.10). Denoting the vector of demodulated pth binoutputs from all M elements asy(p)=[y⁽⁰⁾(p), y⁽¹⁾(p); . . . , y^((M−1))(p)^(T) and the estimated pthsub-symbol is obtained as

{circumflex over (a)}(p)=w ^(H)(p)y(p)  (3.2)

The optimum weight vector for (3.2) above which maximizes the SINR atthe output of the array is given by

w _(opt)(p)=γ(p)R _(y) ⁻¹(p)Λ(p)  (3.3)

where γ(p) is a constant (not affecting the output SINR);R_(y)(p)εC^(M×M) is the received data covariance matrix andΛ(p)=[a_(s,n) ⁽⁰⁾(p), a_(s,n) ⁽¹⁾(p), . . . , a_(s,n) ^((M−1))(p)]^(T)is the propagation vector for the pth bin. R_(y)(p) and Λ(p) areestimated by periodic pilot sub-symbols inserted in the each active bin.A number of techniques can be used for channel estimation (Λ(p)) usingdata directed (i.e. training sequence) or decision directed operationtaking into account the time-frequency dispersive characteristics of thechannel.

In this case, constraint based beamforming is used with the constraintschosen such that their rate of change is significantly slower than thedata rate. This approach also allows for flexible and generalconstraints, albeit at the expense of higher computation required fortheir generation. In the sequel, DOA based constraints are used toexploit angle diversity. To enable the simultaneous exploitation ofspatial and angle diversity, the base-station array is partitioned intomultiple sub-arrays. While the elements within each sub-array areclosely spaced, the individual sub-arrays are spaced far apart. Thisallows for combined use of angle diversity (via constraint basedbeamforming in each sub-array) and spatial diversity (via diversitycombining of all sub-array outputs). Thus, consider a base-stationantenna array configuration comprised of M sub-arrays, each with Selements, for a total of MS elements. For an inter-element spacing of ρand narrowband signal wavelength λ, the fading experienced at adjacentsensors is almost perfectly correlated for sufficiently small values ofρ/λ (such as 0.5 or less) and angle spreading (around 5°-10° or less).Thus, the inter-element spacing within each sub-array ρ is chosen tofacilitate beamforming. A large inter sub-array spacing, on the otherhand, is chosen to obtain a spatial diversity gain. For example, aspacing of 5λ to 10λ or more is regarded to be adequate for obtainingsufficiently low fading correlation. Note that conventional beamformingonly, or diversity combining only arrays are special cases of thisconfiguration with M=1 and S=1 respectively. If maximal ratio combining(MRC) is used for combining outputs from different sub-arrays in thesecond stage, a separate FEQ is not needed. On the other hand, ifswitched diversity combining is used, a FEQ is still required. Thus, ifchannel estimation is to be eliminated, switched diversity isappropriate in conjunction with differential signaling.

For typical cellular systems, the rate of change of DOAs is much lowerthan the symbol rate allowing for the use of only a few pilot bins toobtain DOA estimates. Moreover, each of these constraints can beutilized for multiple bins, thus greatly reducing the totalcomputational burden for constraint generation. Another key advantage ofDOA based beamforming is that since DOA information is independent ofcarrier frequency, the information can be re-used for downlinkbeamforming as well. In addition to conventional algorithms, DOAestimation techniques based on time-difference-of-arrival and multiplebase-station triangulation are also emerging resulting information canalso be utilized for other tasks such as mobile hand-offs andgeolocation.

Extending the notation of the previous section, let the CIR matrix forthe mth sub-array be denoted by H_(u,l) ^((m)). The received signal atthe sth element of the mth sub-array in the presence of spatiallydirectional desired signals and CCI is given by

$\begin{matrix}{r^{({m,s})} = {{\sum\limits_{l = 0}^{L - 1}{c_{l}^{({m,s})}{\sum\limits_{u = 0}^{U - 1}{H_{u,l}^{(m)}T_{l}D_{u,l}a_{l}}}}} + v^{({m,s})} + {\sum\limits_{e}\; {c_{e}^{({m,s})}Z_{e}^{({m,s})}}}}} & (3.4)\end{matrix}$

where c_(l) ^((m,s)) and c_(e) ^((m,s)) denote the multiplicativefactors which can be factored out in the sth sensor's response of themth sub-array with respect to the reference sensor (s=0) for the lthuser and eth CCI component respectively. For instance, if the signalsare assumed to emerge from point sources, c_(l) ^((m,s))=e^(jsØ)p;pεS_(l) and c_(l) ^((m,s))=e^(jsØe) where Ø_(p) and Ø_(e) denote thespatial DOA's given by Ø_(p)2π(ρ/λ)sin(θ_(p)), pεS_(l) andφ_(e)=2π(ρ/λ)sin(θ_(e)) corresponding to DOA's Ø_(p) and Ø_(e) of theparticular desired signal and CCI respectively. If the sources areassumed to be in the array far-field, the directions can be assumed tobe unchanged with respect to each sub-array's reference element. Thus,the demodulated signal at the output of the (m,s)th element is given by

$\begin{matrix}{{y^{({m,s})}(p)} = {{\sum\limits_{l = 0}^{L - 1}{\sum\limits_{u = 0}^{U - 1}{t_{l,p}^{H}c_{l}^{({m,s})}{J^{H}(\eta)}H_{u,l}^{(m)}T_{l}D_{u,l}a_{l}}}} +}} \\{{t_{l,p}^{H}{J^{H}(\eta)}( {v^{({m,s})} + {\sum\limits_{e}\; {c_{e}^{({m,s})}z_{e}^{({m,s})}}}} )}} \\{= {{c_{l}^{({m,s})}{\alpha_{f,\eta}(p)}a} + {i_{f,\eta}^{({m,s})}(p)} + {{\hat{v}}_{\eta}^{({m,s})}(p)} + {\sum\limits_{e}\; {c_{e}^{({m,s})}{{\hat{z}}_{e,\eta}^{({m,s})}(p)}}}}}\end{matrix}$

where a_(f,n)(p) is given by (2.9) and

$\begin{matrix}{{i_{f,n}^{({m,s})}(p)} = {\frac{1}{N}{\sum\limits_{l = 0}^{L - 1}{\sum\limits_{{k \in S_{l}}{k \neq p}}^{\;}\; {\sum\limits_{u = 0}^{U - 1}\; {\sum\limits_{n = 0}^{N - 1}{c_{l}^{(s)}{h_{{n - u},l}^{(m)}(n)}^{j{({{\frac{2\pi}{N}{n{({k - p + \eta})}}} + \beta})}}^{{- j}\frac{2\pi}{N}{uk}}{a(k)}}}}}}}} & (3.5)\end{matrix}$

A single DOA estimation and beamforming processor is shared between allsub-arrays. DOAs of the received signal's dominant path (and possiblyother secondary multipath components) are assigned to sub-arrays toenable computation and update of their respective weight vectors. Also,the same weight vector may be used for more than one sub-array ifsecondary paths are unused (or for economy of implementation). Let w_(b)^((m))(p)εC^(S) denote the pth bin's beamforming vector for the mthsub-array. The mth sub-array output is given by

ā ^((m))(p)=[w _(b) ^((m))(p)]^(H) _(y) ^((m))(p)

where Y^((m))(p)=[y^((m,0))(p), y^((m,1))(p), . . . ,y^((m,S−1))(p)]^(T). We formulate the solution for W_(b) ^((m))(p) usingthe well known generalized sidelobe canceler (GSC) framework. The GSCformulation of the beam-former is particularly useful since it readilylends itself to recursive implementations using standard LMS or RLS typealgorithms, or via block sample covariance matrix inversion. The GSCuses a constrained output energy minimization criterion and under asignal preserving constraint, it yields the corresponding MMSE solutionfor beamformer weights. The constrained optimization problem may beformulated as,

$\begin{matrix}{{w_{b}^{(m)}(p)} = {{\arg \; {\min\limits_{w}\; {w^{H}{R_{y}^{(m)}(p)}w\mspace{14mu} {subject}\mspace{14mu} {{to}\mspace{14mu}\lbrack c^{(m)} \rbrack}_{p}^{H}w}}} = f}} & (3.6)\end{matrix}$

where C_(p) ^((m)) is the constraint matrix whose columns representmultiple constraints; f is the desired constraint response;R_(y) ^((m))(p)=R_(S) ^((m))(p)+R_(i) ^((m))(p)+R_(v)+R_(z) ^((m))(p);R_(y) ^((m))(p)εC^(S×S); is the pth bin's received data covariancematrix for the mth sub-array and R_(s) ^((m))(p), R_(i) ^((m))(p),R_(v)=σ_(v) ²I_(S) and R_(z) ^((m))(p) denote the corresponding signal,IBI, additive noise and CCI covariance matrices respectively. The GSCsolution to (3.6) is well known [9, 25] and is given by

w _(b) ^((m))(p)=w _(q) ^((m))(p)−C _(p,a) ^((m)) w _(a) ^((m))(p) where

w _(b) ^((m))(p)=(C _(p,a) ^((m)) ^(H) R _(y) ^((m))(p)C _(p,a)^((m)))⁻¹ C _(p,a) ^((m)) ^(H) R _(y) ^((m))(p)w _(q) ^((m))(p)  (3.7)

w_(q) ^((m))(p)=C_(p) ^((m))[C_(p) ^((m)) ^(H) C_(p) ^((m))]⁻¹ f andC_(p,a) ^((m))εC^(S×S) ^(c) is the matrix spanning the null space ofC_(p) ^((m)) where S_(c)<S is the number of constraints used. If onlyone signal preserving constraint is used, C_(p) ^((m))=d^(m)(p) and f=1where d^(m)(p) denotes the chosen estimated steering vector of thedesired user. The output SINR with the mth beamformer is given as

${{SINR}^{(m)}(p)} = \frac{\lbrack w_{b}^{(m)} \rbrack^{H}(p){R_{S}^{(m)}(p)}{w_{b}^{(m)}(p)}}{\lbrack w_{b}^{(m)} \rbrack^{H}(p)( {{R_{i}^{(m)}(p)} + R_{v} + {R_{z}^{(m)}(p)}} ){w_{b}^{(m)}(p)}}$

To determine the performance gain which may be obtained from diversitycombining, let the average signal-to-noise ratio per bit at eachsub-array output be denoted by {circumflex over (γ)}b. Assuminguncorrelated Rayleigh distributed received signals, the averageprobability of bit error (P _(b)) for coherent PSK sub-symbols using MRCis given by

$\begin{matrix}{{\overset{\_}{P}{bMRC}} = {\lbrack {P( {\overset{\_}{\gamma}}_{b} )} \rbrack^{M}{\sum\limits_{m = 1}^{M}\; {\begin{pmatrix}{M - 2 + m} \\{m - 1}\end{pmatrix}\lbrack {1 + {P( {\overset{\_}{\gamma}}_{b} )}} \rbrack}^{m - 1}}}} & (3.8)\end{matrix}$

where P(γ _(b)) denotes the probability of error for a specific alphabetsize. For example, if the probability of error in a AWGN channel isgiven by aerfc (√{square root over (bγ_(b))}), then by averaging overthe probability density function of γ_(b) ₃ P(γ _(b))≈a/(2bγ _(b)).Channel estimation for MRC is performed on post-beamforming outputs fromeach sub-array which greatly minimizes the impact of CCI on the channelestimates.

The joint space-frequency bin allocation scheme automatically determinesbin allocations for mobile users taking the spatial dimension intoaccount. Spectral locations are sought for each bin such that the K binsbelonging to any one user are spaced as far apart in frequency aspossible to minimize mutual IBI. Spacing the bins belonging to each userover a range of frequencies also increases frequency diversity (i.e.,because the bins of a particular user are spaced in frequency, typicalCCI sources operating in a small, in-band frequency range have lesseffect on the overall signal then if the signal bins were closelygrouped in frequency.) Also, each bin is co-located with bins belongingto other users which are spaced as far apart as possible in the DOAs oftheir signals. This enables the beamformer to suppress IBI betweenadjacent bins by exploiting spatial selectivity.

Accomplishing these goals simultaneously at each arrival (when a newuser requires bin allotment) or departure (when an existing userterminates its connection) can be a computationally formidable task dueto the typically large number of bins and users. The following method isproposed which sequentially solves the above problem in an efficientmanner. To begin, the entire spectrum is partitioned into K contiguousfrequency blocks containing L bins. Every user is allotted one bin ineach of the K blocks and spatial information is used to determine thebin distribution within each block. If ψ denotes the dominant DOA of thepth user, the bin placement in any one block is done by computing thefollowing metric for each available bin as

$\begin{matrix}{n_{p} = {\arg \; {\max\limits_{n}{\min\limits_{i}\; {\Delta\theta}_{n,i}}}}} & (3.9)\end{matrix}$

where

Δθ_(n,i)=|ψ−θ(n−i)| iε[−W,W], i≠0  (3.10)

is the magnitude of the differences between ψ and DOAs of bins in aneighborhood of 2 W bins. If an adjacent bin is unoccupied, thecorresponding value of Δθ_(n,i) is set to the maximum angular differencepossible. Note that owing to the block structure of bin allocation, itis sufficient to compute the metric for any one block and replicate thebin allotment in the remaining blocks. Moreover, the objective functionis easy to compute and can be maintained in a tabular form for fastlook-up. These metrics are updated whenever there is an user arrival ordeparture. In severe multipath environments, the spatial selection isgeneralized by taking into account multiple DOAs as well as power levelsfor each bin in computing the windowed DOA difference for each bin. Inpropagation environments dominated by CCI instead of (or in addition to)IBI, the above criterion can take into account the DOA and frequencylocation, as well as signal strength, of co-channel interferes tominimize the effect of spectral overlap and leakage.

Consider an example wireless OFDM system with the following parameters:Total number of bins N=256; Useful symbol time T_(sym)=230 μs; CP orguard time T_(CP)=20 μs and symbol rate f_(sym)=1/(T_(sym)+T_(CP))=4KHz. Thus, inter-bin spacing f_(bin)=1/T_(sym)=4.348 KHz and totaloccupied bandwidth=N f_(bin)=1.11 MHz. QPSK modulated sub-symbols, theaggregate data rate=(2 bits/bin) (N bins/symbol) (f_(sym)symbols/sec)=2.048 Mb/s. For an uplink multiple access system with theseparameters, L=32 independent users, each with K=8 bins can each beallocated a raw bit rate of 64 Kb/s. Other factors which can reduce theuser available data rate or the number of usable bins include analog anddigital filtering constraints, spectral mask requirements, and bandwidthoverhead for control and signaling.

Consider first the effect of IBI only without additive noise and CCI. Inthis application, it is known that M-branch spatial diversity using MRC(M=2,3) is very effective. This result is not surprising since IBI isspatially and spectrally distributed and MRC is known to be the optimumarray combining method in the presence of noise only.

Consider now the performance of maximum SINR optimum array combining forCCI suppression on a slowly time-varying channel. For M=2, both MRC andmaximum SINR combining yield similar performance and a 3 dB arrayprocessing gain is obtained for AWGN only (when CCI is negligible).

For a given number of total elements, combined use of angle and spatialdiversity is clearly superior to angle diversity alone. It is known thatdiversity combining is most beneficial at relatively higher input SINRswhile beamforming is most effective for relatively lower SINR's when theinterference is strong.

Now the discussion turns to the preferred embodiment. FIG. 1 illustratesone exemplary embodiment of an adaptive array architecture 10 with abase station 7 implemented for use in a cellular telephone systemaccording to the present invention. An array support structure 1, may beimplemented as single or multiple towers as shown or by any other meansthat enable the array to be placed at the desired elevation and spacingincluding but not limited to conical towers or fixation on commercialbuildings of sufficient elevation. In the preferred embodiment, thearray support structure 1 is attached to the array fixation structure 2by means of support beams 3. The array support structure 1 thusmaintains the array fixation structure 2 at a fixed elevation. In theembodiment of FIGS. 1a-1c , the array fixation structure 2 is arrangedin the shape of a triangle, thereby dividing the complete 360° servicearea into three sectors of 120° each. By way of example only, each 120°sector constitutes a single adaptive array 4, each adaptive array beingcomprised, by way of example only, of two sub-arrays 5. The arraystructure may be varied without departing from the present invention.For instance, the service area may be divided into differing numbers ofsectors, the number of sub-arrays may be increased or their orientationchanged. The sectors need not be equal in size. Each sub-array iselectrically connected to a base station 7 which may be located on thetower as shown or at any other convenient location including mounted onthe array fixation structure 2 or in an enclosed area at the base of thearray support structure 1.

In each adaptive array, the sub-arrays are separated by a distancesufficient to allow the resultant signals from each sub-array to bespatial diversity combined. Spatial diversity requires a sufficientelement spacing to allow independent fading at different elements. Thesignals from such spaced elements can be combined to lessen interferenceand increase the received signal strength. For spatial diversitycombining to be effective at the effective operating distance of acellular telephony system, an array spacing of at least 2 wavelengths atthe frequency of operation is beneficial with the spacing preferablybetween 5 to 15 wavelengths. In the present embodiment, the minimumgroup spacing 12 is in the range of 5-15 wavelengths.

FIGS. 2a and 2b , illustrate exemplary geometries that may be used forthe sub-arrays 5. By way of example only, the sub-array 5 may beimplemented as a dipole array 20 comprised of three antenna arrayelements 21 oriented vertically and arranged side-by-side. The spacingbetween the antenna array elements 21, in this embodiment, is less thana predetermined maximum element spacing, for example, one half of onewavelength at the frequency of operation (<λ/2) to facilitate steering.Steering or beamforming is the ability of the signal response of anarray to be altered through modification of the timing or phasing of thearray elements; for instance, by altering the phasing of array elementsthe array can be made to receive desired user signals at a higher gainwhile at the same time damping undesired interference signals. Toprovide effective steering, the elements should be spaced as close aspossible; the element spacing must be less than a wavelength andclassically less and one-half of one wavelength to provide steering.

The antenna array elements 21 are attached to and supported by the arrayfixation structure 2 and are electrically connected to the base station7. Sub array 5 may also be implemented as a microstrip patch array 25(as shown in FIG. 2b ). Microstrip patch array 25 may be configured as aButler array comprised of eight total patches 26 arranged in patch rows27 of four patches each. As in the dipole array 20, each of the patches26 in a given patch row 27, in this preferred embodiment is separated byless than the maximum element spacing, for example, one half of onewavelength at the frequency of operation (<λ/2) to facilitate steering.Alternatively, the microstrip patch array 25 may be replaced with twodipole antenna elements arranged horizontally side-by side that providea similar signal response pattern. The microstrip patch array 25 may bedesirable because of its low manufacturing cost in some applications.

FIG. 3 graphically illustrates the manner by which the base station 7determines the direction of arrival of either a remote unit or a sourceof co-channel interference. FIG. 3 illustrates a top view of a sub-array5 comprised of two antenna array elements 31 a and 31 b arrangedvertically side-by-side with a separation less than λ/2. Incoming signal33 from a user impinges upon the antenna array elements 31 a and 31 b.Because the incoming signal 33 is arriving from a large distancerelative to the separation between elements 31 a and 31 b, the far fieldapproximation (the signal source is so far away from the receiver thatthe incident waves appear as plane waves) is valid and the incomingsignal 33 can be approximated as impinging upon array elements 31 a and31 b at the same angle. The additional signal travel distance 37 thatthe incoming signal 33 must travel to impinge upon the more distant ofthe array elements 31 b can be calculated in several ways, such as bytime delay or phase shift. Because the separation between the arrayelements 31 a and 31 b is also known, the sine of angle 38 may becalculated and is equal to direction of arrival 39 normal to thesub-array 5. The base station 7 then computes the direction of arrival.

Although the exemplary embodiment contains two elements, it will beobvious to those skilled in the art that the direction of arrival may becalculated by means of many different methods. The accuracy of thedetermination of the direction of arrival is, of course, dependant uponthe method used. In general, a greater number of antenna elements canprovide greater resolution of the direction of arrival. Thus, the BS 7could distinguish between remote users who are disposed closer togetherwith regard to direction of arrival. Determination of the direction ofarrival is also dependant upon the filter used by the BS 7. In addition,the BS 7 may utilize a number of multipath signals to determinedirection of arrival. In this case, the direction of arrival may bealong multiple paths.

FIGS. 4a and 4b illustrate a general signal response pattern in FIG. 4aas well as, in FIG. 4b , a signal response pattern modified to providehigher gain to desired user signals while damping interference. FIG. 4billustrates exemplary signal response patterns of the adaptive array,along with multi-path reception and co-channel interference. Multi-pathreception refers to an individual user's signal that is received by theBS from more than one direction such as when user signals are reflectedfrom structures in an urban environment. Co-channel interference (CCI)refers to any other undesired signal whose spectrum overlaps with thespectrum of the particular OFDM system under consideration and causesinterference. An idealized signal response pattern is shown as 40 a inFIG. 4a . The radius of the response pattern 40 a from the BS 7 in agiven angular direction indicates the relative gain or signal responselevel of the BS in that radial direction. Thus, in the response pattern40 a, the adaptive array receives with equal strength signals from anydirection. This response may be seen to be less than ideal whenoperating in the presence of CCI because it is desired to minimize or“damp out” undesired CCI to increase system performance while increasingthe gain for desired signals. Thus, the signal response pattern 40 a isaltered.

Once the direction of arrival of a communications signal from a remoteuser is known, the array elements can be energized or their responsesplaced through a filter with varying phases, time delays or both toproduce the signal response pattern 40 b in FIG. 4b . Again, in signalresponse pattern 40 b the radial distance of the response pattern 40 bfrom BS 7 is indicative of the relative gain or signal response level inthat radial direction. The BS 7 modifies the idealized pattern toprovide increased gain for the signals of user 41 as well as themulti-path propagation 11, 12 of the signals of user 41. At the sametime, the signal response pattern provides for damping in the directionof CCIs 13 and 14 to minimize received interference signals.

FIG. 5 is a block diagram of an exemplary embodiment of a receiver 60for the adaptive antenna array architecture 10. The receiver 60 iscapable of correcting for incoming channels which experience fasttime-varying fading. The receiver 60 illustrates two stages of an array.Signals from mobile users 51 impinge upon the adaptive array 52comprised of a plurality of sub-arrays 59 numbered 0 to M. Eachsub-array 59 comprises a plurality of elements 54 numbered 0 to S. Thenumber of elements 54 in each sub-array 59 may not be equal. Eachsub-array 59 can handle signals from many mobile users 51 at the sametime. At each sub-array 59, the signals from mobile users 51 passthrough coherent demodulators to beamformers 56 which are supplied withdirection of arrival data from the DOA processor 57 in the BS 7 toconstruct the desired signal response pattern. The DOA processor 57calculates the direction of arrival in accordance with the methoddescribed above in connection with FIG. 3. The output signals from thebeamformers 56 are passed through a spatial diversity combiner 58 toremove interference. The output signal from the spatial diversitycombiner 58 may be fed into a standard voice or data network.

In an alternative embodiment, the adaptive antenna array architecture 10may be used in an orthogonal frequency division multiple access (OFDMA)system. The base station 7 determines the direction of arrival (DOA) inthe manner described above. The base station 7 of the OFDMA systemsegments the available bandwidth into multiple frequency bins which canthen be allocated based on predetermined factors. The inclusion of theDOA as a factor in an OFDMA bin allocation scheme improves overallsystem performance by allowing the OFDMA bin allocation algorithm todifferentiate between user signals on the basis of DOA as well asdifferentiate between the DOA of CCIs and user signals thus providingfor less overall CCI and Inter-Bin Interference (IBI).

FIG. 6 illustrates an exemplary operating environment for a cellularsystem. The base station 7 is operating in the presence of signals fromco-channel interferer 61 and signals from mobile users 62 a, 62 b, 62 c,62 d and 62 e.

The direction of arrival of all signals relative to the base station 7can be observed as lines leading from the spatial positions of thevarious signals to BS 7. Mobile users 62 c and 62 b have substantiallythe same DOA, while mobile user 62 e and co-channel interferer 61 havesubstantially the same DOA. Also, mobile users 62 a, 62 b, and 62 c arelocated at approximately the same distance from base station 10 and thushave approximately the same signal strength. While, in this exemplaryenvironment, the DOA of each signal is shown as a straight line from theremote unit, in a more complex implementation of the present inventionthe BS 7 may take into account multipath signals of a certain magnitude(usually not more than two or three signal paths for computationalsimplicity) as well as accounting for the angle spreads of the incidentsignal wither directly from the remote unit or multipath. For ease ofrepresentation, the exemplary environment of FIG. 6 shows dominant,straight-line signal paths from the remote units and the CCI withoutangle spreading.

FIG. 7 illustrates a frequency band distribution according to anembodiment of the present invention. The frequency band 70 of the OFDMAsystem, expressed in the frequency domain as shown, includes the rangeof frequencies between the bottom or low frequency cut-off 72 a and thetop or high frequency cut-off 72 b. In an OFDMA system, the frequencyband 70 is segmented into bins 73 for allocation to individual userswhich are then grouped into neighborhoods 71 a and 71 b (e.g., three tofive bins per neighborhood) which are shown as an outtake 76 of thefrequency band 70. However, the use of neighborhoods in the presentembodiment is done for the sake of computational convenience. Althoughthe preferred embodiment above was implemented using groupings offrequency bins called neighborhoods, many aspects of the preferredembodiment can be implemented without grouping the frequency bins inthis way. In this case, the above preferred embodiment may operate in adifferent fashion, such as on a bin-by-bin basis, to accomplish theinvention.

Once the bins 73 and neighborhoods 71 a and 71 b have been established,the BS 7 allocates bins to a particular user so as to maximize theoverall system performance. An noted above, two significant constraintson performance are Inter-Bin Interference (IBI) and Co-ChannelInterference (CCI). In an effort to minimize IBI and CCI, the BS 7continually monitors a number of parameters and uses them to compute thebin allocations for a given user. Bin allocations are typically made atstart-up, but can also be changed dynamically throughout operation inresponse to changes in the prevalent noise, interference, or fadingconditions.

FIG. 8 is a block diagram illustrating an exemplary method forallocating frequency bins by the BS 7 according to one embodiment of thepresent invention. To illustrate the bin selection method of the BS 7,it is assumed that the mobile users of FIGS. 6 and 7 are to be allocatedK bins each. Using these inputs, the BS 7 allocates bins to satisfy thefollowing (desired) criteria.

First, the BS 7 determines which bins are available to be allocated atstep 80. Bins are not available to be allocated if the bin is in use byanother user or the level of CCI is to high to provide adequate usersignal resolution. For instance, in FIG. 7, the presence of CCI isindicated by frequency artifact 74. The bins thus dominated by CCI willnot be available to be allocated at this stage. The BS 7 then determinesif enough open bins exist to support a remote user seeking registrationwith the system or seeking to use more frequency bins (step 81). If theBS 7 can not allocate enough bins, the BS 7 analyzes (step 82) thepreviously rejected high-CCI bins 75. If the difference in the DOA's ofthe particular user and the co-channel interferers are sufficientlylarge to permit data to be carried in the bin 75, the BS 7 allocates thebin 75 to the user 82. Referring to FIG. 6, if the frequency artifact 74is the output of CCI 61, the bins 75 dominated by frequency artifact 74may be allocated to users 62 a, 62 b, 62 c, or 62 d because the DOA ofthese users is widely different from CCI 61 (FIG. 6). However, such CCIbins 75 could not be allocated to user 62 e because the DOA of user 62 eand CCI 61 are substantially similar. After determining what additionalCCI bins 75, if any, may be allocated to the given user, the BS 7 thendetermines if sufficient bins now exist after the CCI-bin 75 allocationto support the user 83. If sufficient bins still do not exist, serviceis refused at step 84.

If sufficient bins exist to support the user, control passes to step 85where the K bins belonging to any one user are spaced as far apart infrequency as possible to minimize mutual IBI. Thus, if many bins in thesystem are open, the bins used to carry data may be separated by severalbins to reduce IBI.

Next at step 86, each bin is placed by the BS 7 in a neighborhood withbins belonging to other users which are spaced as far apart as possiblein the dominant DOAs of their signals. Stated another way, the BS 7collects unique sets of bins 71 a and 71 b as neighborhoods such thateach frequency bin in a given neighborhood is assigned to remote usershaving substantially different DOAs. For example, a 3-5 bin neighborhoodmay be used. For example, FIG. 7 illustrates two 5-bin neighborhoods 71a and 71 b. Applying the user DOAs from FIG. 6 and assuming for a momenta system in which only two neighborhoods exist and assuming all signalsare of similar received power levels, ideally, the signals from user 62c may be placed in bins distant from the signals from user 62 b becauseof the similarity in their DOAs. An ideal bin placement under thisconstraint would maximize the differences between the DOAs of successivebins for an overall neighborhood as shown in neighborhood 71 b. Thesignal from user 62 e is places in the bin between the signals fromusers 62 b and 62 c and the signals from users 62 a and 62 d are placedin successive bins as shown. This method of bin placement serves toreduce overall IBI. Spacing the bins belonging to each user over a widerange of frequencies within the band also provides frequency diversity.Frequency diversity is desirable because it serves to lessen the effectsof fading over a certain frequency range. For example, by allocatingmany widely spaced frequency bins to a single user, if the operatingenvironment is such that some of the bins experience fading, the overallsignal quality will still remain high because the other user bins willnot experience this fading; in short, fading over a small frequencyrange within the band will not effect the whole signal.

Next, at step 87, the BS 7 reevaluates the bin allocations. The BS 7determines whether to place each bin in a neighborhood with binsbelonging to other users such that differences in received signal powerlevel of active bins in the neighborhood are minimized. By re-assigningbins according to signal power, the BS 7 ensures that weaker moredistant signals are not overpowered by closer more powerful signals.Thus, turning to FIG. 6 for reference, the closer, stronger signals ofusers 62 e and 62 c may be grouped together, in a first group, while themore distant, weaker signals of users 62 a, 62 b, and 62 d may be placedin a second group spaced from the first in frequency band 70.

The above method leads to better separation of potentially interferingsignals in the spatial domain, thus facilitating the operation ofspatial interference suppression. Optionally, the above criteria ofsteps 81-87 may be “weighted” differently, or considered in differentorders to construct methods optimized for a specific (or category of)channel and interference scenarios, as will be apparent to those skilledin the art. In the example of FIG. 8, because IBI is a dominantimpairment, spacing bins in frequency and placing bins in a neighborhoodwith bins of differing DOAs is given the most importance. Depending onthe environment, other criteria may become dominant. For instance, thebins in a neighborhood may be placed with bins of similar power if thesystem is operated in an environment with a wide range of receivedsignal power levels.

Since the number of bins in an OFDM system can be quite large (severalhundred bins), the implementation of the overall method may bereasonably simple to enable execution in real-time. For instance, onepossible way to implement it would be to construct a data structurewhich contains a table of information about each bin. The table mayinclude information such as whether the bin is active or inactive (atany given time), the user occupying the bin if any, whether it is adata, control or pilot bin, modulation scheme and constellation size ofsub-symbols in the bin, received power level, dominant DOA's of the useroccupying the bin, power level and DOA's of any CCIs spectrallyoverlapping with the bin etc. Optionally, all items of the aboveinformation may not be available for each bin at all times. Certainitems in the above table like received power levels appear to be bestsuited for update on a periodic (scheduled) basis (such as every nmilliseconds or during each frame as per some existing framingstructure). Other items are better suited for update in an event drivenmode (such as user activity and constellation size), e.g., when a userarrives, departs, requests (or is forced to have) a change in the amountof allocated bandwidth.

Thus, through the use of the above factors, individual bins may bedynamically allocated and re-allocated on-the-fly. The composition ofneighborhoods may also be changed dynamically if a trigger event, suchas the advent of a new CCI source should arise. The number of binsassigned to a neighborhood may also change.

Although the preferred embodiment above was implemented using groupingsof frequency bins called neighborhoods, many aspects of the preferredembodiment can be implemented without grouping the frequency bins inthis way. In this case, the above preferred embodiment may operate in adifferent fashion, such as on a bin-by-bin basis, to accomplish theabove invention. For instance, if frequency bins are not grouped intoneighborhoods, allocating on a bin-by-bin fashion such that the DOAs ofadjacent or approximately adjacent bins differ would accomplish theabove invention. In this fashion, the above invention may also beimplemented with neighborhoods containing only one bin.

Although the present invention has been described with reference tospecific embodiments, those of skill in the art will recognize thatchanges may be made thereto without departing from the scope and spiritof the invention as set forth in the appended claims.

1. (canceled)
 2. An apparatus for processing signals received from aplurality of remote transceivers, the apparatus comprising: at least twobeamformers, each beamformer being associated with a correspondingantenna sub-array having at least two antenna elements, and beingconfigured to: receive at least two base signals associated with atleast two carrier signals corresponding to a frequency bin allocated toa user, the at least two carrier signals being associated with the atleast two antenna elements of the corresponding antenna sub-array; andgenerate an output signal based on the at least two base signals anddirections of arrival of the at least two carrier signals; and a spatialdiversity combiner configured to selectively combine at least two outputsignals of the at least two beamformers.
 3. The apparatus as recited inclaim 2 further comprising a direction of arrival estimator configuredto estimate directions of arrival of the at least two carrier signals.4. The apparatus as recited in claim 2, wherein the spatial diversitycombiner is selected from a group consisting of: a maximal ratiocombiner, a maximum signal to interference and noise ratio (SINR)optimum combiner, and a switched diversity combiner.
 5. The apparatus asrecited in claim 2, wherein the at least two beamformers and the spatialdiversity combiner are implemented via a general purpose processor, anapplication-specific processor, an application-specific integratedcircuit, general purpose processor executing application specificsoftware, or a combination thereof.
 6. An apparatus for processingsignals received from a plurality of remote transceivers, the apparatuscomprising: at least two beamformers, each beamformer being configuredto adjust gains applied to at least two base signals, associated with atleast two carrier signals, based on directions of arrival of the atleast two carrier signals, and to produce at least one output signal; aspatial diversity combiner configured to selectively combine at leasttwo output signals of the at least two beamformers; and a processorconfigured to: monitor information related to a plurality of frequencybins; and assign signals associated with a remote transceiver tofrequency bins based on at least one of association with a remotetransceiver and direction of arrival.
 7. The apparatus as recited inclaim 6, wherein the processor is further configured to assign thesignals to neighboring frequency bins in a manner that reducesdifferences in power between the assigned signals:
 8. The apparatus asrecited in claim 6, wherein the processor is further configured toassign the signals in a manner to avoid having a co-channel interferingsignal in a neighboring frequency bin.
 9. The apparatus as recited inclaim 6, wherein the processor is further configured to assign thesignals based on an indication of neighboring frequency bins includingat least one of the following: an adjacent frequency bin and anapproximately adjacent frequency bin.
 10. A method for processingsignals received from a plurality of remote transceivers, the methodcomprising, in a receiver system including a spatial diversity combinerand at least two beamformers: receiving a plurality of base signals fromat least two antenna sub-arrays each having at least two antennaelements, the plurality of base signals associated with at least twocarrier signals corresponding to a frequency bin allocated to a user,the at least two carrier signals being associated with the at least twoantenna elements of the corresponding antenna sub-array; receiving theplurality of base signals to one of the at least two antenna sub-arraysby an associated one of the at least two beamformers; receiving theplurality of base signals to another of the at least two antennasub-arrays by an associated one of the at least two beamformers;generating, by each beamformer of the at least two beamformers, anoutput signal based on the at least two of the plurality of base signalsand directions of arrival of the at least two corresponding carriersignals; and spatial diversity combining the output signals of the atleast two beamformers.
 11. The method as recited in claim 10, furthercomprising estimating directions of arrival of the at least two carriersignals.
 12. The method as recited in claim 10, wherein spatialdiversity combining is selected from a group consisting of: maximalratio combining, maximum signal to interference and noise ratio (SINR)optimum combining, and switched diversity combining.
 13. A method forprocessing signals received from a plurality of remote transceivers, themethod comprising: forming signal beams using base signals, each signalbeam being formed using at least two base signals, associated with atleast two carrier signals, based on directions of arrival of the atleast two carrier signals; spatial diversity combining at least two ofthe formed signal beams; monitoring information related to a pluralityof frequency bins; and assigning signals associated with a remotetransceiver to frequency bins based on at least one of association witha remote transceiver and direction of arrival.
 14. The method as recitedin claim 11, wherein assigning the signals further includes: assignmentof signals to neighboring frequency bins is performed in a manner thatreduces differences in power between the assigned signals.
 15. Themethod as recited in claim 11, wherein assigning the signals furtherincludes: assignment of signals to frequency bins is performed in amanner that avoids having a co-channel interfering signal in aneighboring frequency bin.
 16. The method as recited in claim 11,wherein assigning the signals is based on an indication of neighboringfrequency bins being at least of the following: an adjacent frequencybin and an approximately adjacent frequency bin.
 17. A non-transitorycomputer readable medium including computer software for processingsignals received from a plurality of remote transceivers, the computersoftware when executed by a processor causes an apparatus to: receive atleast two base signals associated with at least two carrier signalscorresponding to a frequency bin allocated to a user, the at least twocarrier signals being associated with at least two antenna elements of acorresponding antenna sub-array; and generate an output signal based onthe at least two base signals and based on directions of arrival of theat least two carrier signals; and spatial diversity combine the outputsignals of at least two beamformers.
 18. The non-transitory computerreadable medium as recited in claim 17, wherein the computer softwarewhen executed by the processor further causes the apparatus to estimatedirections of arrival of the at least two carrier signals.
 19. Thenon-transitory computer readable medium as recited in claim 17, whereinthe computer software when executed by the processor further causes theapparatus to spatial diversity combine the output signals based onmaximal ratio combining, maximum signal to interference and noise ratio(SINR) optimum combining, or switched diversity combining.
 20. Thenon-transitory computer readable medium as recited in claim 17, whereinthe computer software, when executed by the processor, further causesthe apparatus to: monitor information related to a plurality offrequency bins; and assign signals associated with a remote transceiverto frequency bins according to at least the following criteria: (1)signals associated with a remote transceiver are assigned frequency binsthat are separated apart in the frequency domain and (2) signalsassigned to neighboring frequency bins that have substantially differentdirections of arrival.
 21. The non-transitory computer readable mediumas recited in claim 20, wherein the criteria further include: assignmentof signals to neighboring frequency bins is performed in a manner thatreduces differences in power between the assigned signals.